One Algorithm to Rule Them All

As a student in a 1970s high school computer lab, I used a teletypewriter to punch holes in a paper tape, dialed a phone number and jammed the handset into two rubber cups. Somehow, an unseen computer on the other end was told to do something. I didn’t see the point.

In the 1980s, as a newly minted account executive at a consulting firm, I assured a colleague I’d be dead long before any work I might do would require learning to use a computer. (I also was sure that I wouldn’t ever need to type — that’s what the secretaries outside my office did on electric typewriters, which corrected typos at the touch of key!)

Unlike me, Domingos did not miss the import of computer science. While I was ignoring the computers popping up on every desktop, he was earning a licenciatura in electrical engineering and computer science from Instituto Superior Técnico at University of Lisbon. After reading about machine learning in a book on artificial intelligence, he skipped an MBA and, instead, earned a Ph.D. in information and computer science from the University of California at Irvine. Now a professor at the University of Washington, Domingos is a leading expert in the fields of machine learning and data mining. His Tolkienesque quest — and the book’s subject — is the most powerful algorithm of all.

This master algorithm, which many experts, whose views Domingos gives a hearing, think is a Computer Age chimera, would allow machines to learn without human assistance. “Every algorithm has an input and an output: the data goes into the computer, the algorithm does what it will with it, and out comes the result,” Domingos explains. “Machine learning turns this around: in goes the data and the desired result and out comes the algorithm that turns one into the other.”

Learning algorithms are already commonplace. Netflix uses them to pick movies for us; Amazon to recommend books; Google to search out Web pages. But Domingos is pursuing something much more far-reaching. “In fact, the Master Algorithm is the last thing we’ll ever have to invent because, once we let it loose, it will go on to invent everything that can be invented,” he writes. “All we need to do is provide it with enough of the right kind of data, and it will discover the corresponding knowledge.”

The corresponding knowledge includes a cure — or, more accurately, myriad cures — for cancer. In theory, Domingos argues, the master algorithm could create a program capable of spitting out the exact formula for a therapy designed to kill a specific patient’s cancer — based on a tumor’s genome, the patient’s medical history and profile, and a “vast database of molecular biology.” Unfortunately, little of the data necessary to fuel such a program exists as yet. But then neither does the master algorithm.

Most of The Master Algorithm is devoted to a survey of the progress that has been made in the quest to discover the one algorithm that could end up ruling us all. Currently, the quest is made up of five expeditions — each led by advocates of one of the major schools of thought in the academy of machine learning: symbolists, who see learning as a process of inverse deduction; connectionists, who think the answer lies in reverse engineering the human brain; evolutionaries, who are looking to genetics and evolutionary biology; Bayesians, who are following a path of probabilistic inference and statistics; and analogizers, who think learning will come from “extrapolating similarity judgments.” Domingos devotes a chapter to each, making a heroic effort to explain the respective lens though which each views the challenge, and their benefits and flaws in layman’s terms. (It’s a task at which he does not always succeed.) The master algorithm, he believes, will be derived from a synthesis of these five schools.

Pedro Domingos assays the state of machine learning and tracks the quest to discover the ultimate algorithm.

Perhaps the most fascinating element of the book is the glimpse it gives us into the mind of its author. In the final chapter, Domingos envisions the world after the discovery of the master algorithm. As might be expected of a computer scientist, this world has utopian overtones.

Whoever discovers the master algorithm would give it away as an open source program. (Thanks!) And the data needed to fuel the algorithm would become the world’s most valuable asset. Every human being would have an ethical responsibility to provide personal data to this global mind. (If cancer patients didn’t share their tumor genomes and treatment histories, for example, curing cancer would not be possible.) Domingos sees an industrial ecosystem arising to both execute this imperative and protect us from abuse. Corporate hosts would collect our data, sell it for us, and ensure that we weren’t mistreated in the process. The money we make selling our data would replace our jobs — many of which would disappear as machines learn to do them. If corporations tried to rook us, data unions (like the labor unions of the Industrial Era) might arise and ensure we are properly compensated.

There is a dark lining to the silver cloud. “What we’ll likely see is unemployment creeping up, downward pressure on the wages of more and more professions, and increasing rewards for the fewer and fewer that can’t yet be automated,” Domingos writes. But that’s OK, because the digital downtrodden can vote. “When the unemployment rate rise above 50 percent, or even before, attitudes about redistribution will radically change,” he adds. “The newly unemployed majority will vote for generous lifetime unemployment benefits and the sky-high taxes needed to fund them. These won’t break the bank because machines will do the necessary production.”

This rather bloodless view of the disruption created by the master algorithm may give you pause, but I’m not worried. I’ll be long dead before it’s discovered.

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